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<a href="#nested-classes">Classes</a> &#124;
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<a href="#pub-methods">Public Member Functions</a> &#124;
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<div class="title">detectNet Class Reference<div class="ingroups"><a class="el" href="group__deepVision.html">DNN Vision Library (jetson-inference)</a> &raquo; <a class="el" href="group__detectNet.html">detectNet</a></div></div>  </div>
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<p>Object recognition and localization networks with TensorRT support.  
 <a href="classdetectNet.html#details">More...</a></p>

<p><code>#include &lt;<a class="el" href="detectNet_8h_source.html">detectNet.h</a>&gt;</code></p>
<div class="dynheader">
Inheritance diagram for detectNet:</div>
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<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a>
Classes</h2></td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structdetectNet_1_1Detection.html">Detection</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Object <a class="el" href="structdetectNet_1_1Detection.html" title="Object Detection result. ">Detection</a> result.  <a href="structdetectNet_1_1Detection.html#details">More...</a><br /></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-types"></a>
Public Types</h2></td></tr>
<tr class="memitem:a29e74cde23a8dd541dbd848e457663d6"><td class="memItemLeft" align="right" valign="top">enum &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#a29e74cde23a8dd541dbd848e457663d6">OverlayFlags</a> { <a class="el" href="classdetectNet.html#a29e74cde23a8dd541dbd848e457663d6a966eaaaa69ed08d36dcb862e3fc644bf">OVERLAY_NONE</a> = 0, 
<a class="el" href="classdetectNet.html#a29e74cde23a8dd541dbd848e457663d6aff23b9098d5bf0f91c31954001c2ff68">OVERLAY_BOX</a> = (1 &lt;&lt; 0), 
<a class="el" href="classdetectNet.html#a29e74cde23a8dd541dbd848e457663d6a7eb5454e7a6dd53d90c2ddd33b00d049">OVERLAY_LABEL</a> = (1 &lt;&lt; 1), 
<a class="el" href="classdetectNet.html#a29e74cde23a8dd541dbd848e457663d6af7ce69cd21d84fb3b963713da66756eb">OVERLAY_CONFIDENCE</a> = (1 &lt;&lt; 2)
 }<tr class="memdesc:a29e74cde23a8dd541dbd848e457663d6"><td class="mdescLeft">&#160;</td><td class="mdescRight">Overlay flags (can be OR'd together).  <a href="classdetectNet.html#a29e74cde23a8dd541dbd848e457663d6">More...</a><br /></td></tr>
</td></tr>
<tr class="separator:a29e74cde23a8dd541dbd848e457663d6"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a8912b56ba825368c4911e315f4f207e4"><td class="memItemLeft" align="right" valign="top">enum &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#a8912b56ba825368c4911e315f4f207e4">NetworkType</a> { <br />
&#160;&#160;<a class="el" href="classdetectNet.html#a8912b56ba825368c4911e315f4f207e4a46004cf6402155cfac9107356b5e7148">CUSTOM</a> = 0, 
<a class="el" href="classdetectNet.html#a8912b56ba825368c4911e315f4f207e4a0be700962b9af1014273d6c51274d717">COCO_AIRPLANE</a>, 
<a class="el" href="classdetectNet.html#a8912b56ba825368c4911e315f4f207e4ab5ce7152df406d0e699fb47c29cda175">COCO_BOTTLE</a>, 
<a class="el" href="classdetectNet.html#a8912b56ba825368c4911e315f4f207e4aa9c3e7f707f38390b8ebe551a6844efd">COCO_CHAIR</a>, 
<br />
&#160;&#160;<a class="el" href="classdetectNet.html#a8912b56ba825368c4911e315f4f207e4afb5c869902acb688cf8b4d7e334bc0f4">COCO_DOG</a>, 
<a class="el" href="classdetectNet.html#a8912b56ba825368c4911e315f4f207e4aba129790ea4695b8fc94f0eeecb344de">FACENET</a>, 
<a class="el" href="classdetectNet.html#a8912b56ba825368c4911e315f4f207e4a6e6da8c4ab8ff441042f7922082be8c3">PEDNET</a>, 
<a class="el" href="classdetectNet.html#a8912b56ba825368c4911e315f4f207e4abddff26f00980b719858a6cf6fb71f53">PEDNET_MULTI</a>
<br />
 }<tr class="memdesc:a8912b56ba825368c4911e315f4f207e4"><td class="mdescLeft">&#160;</td><td class="mdescRight">Network choice enumeration.  <a href="classdetectNet.html#a8912b56ba825368c4911e315f4f207e4">More...</a><br /></td></tr>
</td></tr>
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</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:a82c0e177e0da142fa22aa7580077307d"><td class="memItemLeft" align="right" valign="top">virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#a82c0e177e0da142fa22aa7580077307d">~detectNet</a> ()</td></tr>
<tr class="memdesc:a82c0e177e0da142fa22aa7580077307d"><td class="mdescLeft">&#160;</td><td class="mdescRight">Destory.  <a href="#a82c0e177e0da142fa22aa7580077307d">More...</a><br /></td></tr>
<tr class="separator:a82c0e177e0da142fa22aa7580077307d"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac2144a8af64dce65e391ccb6ab0fb5f9"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#ac2144a8af64dce65e391ccb6ab0fb5f9">Detect</a> (float *input, uint32_t width, uint32_t height, <a class="el" href="structdetectNet_1_1Detection.html">Detection</a> **detections, uint32_t overlay=<a class="el" href="classdetectNet.html#a29e74cde23a8dd541dbd848e457663d6aff23b9098d5bf0f91c31954001c2ff68">OVERLAY_BOX</a>)</td></tr>
<tr class="memdesc:ac2144a8af64dce65e391ccb6ab0fb5f9"><td class="mdescLeft">&#160;</td><td class="mdescRight">Detect object locations from an RGBA image, returning an array containing the detection results.  <a href="#ac2144a8af64dce65e391ccb6ab0fb5f9">More...</a><br /></td></tr>
<tr class="separator:ac2144a8af64dce65e391ccb6ab0fb5f9"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:af2f0dd5c2b47daa57953d3b5e1e83767"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#af2f0dd5c2b47daa57953d3b5e1e83767">Detect</a> (float *input, uint32_t width, uint32_t height, <a class="el" href="structdetectNet_1_1Detection.html">Detection</a> *detections, uint32_t overlay=<a class="el" href="classdetectNet.html#a29e74cde23a8dd541dbd848e457663d6aff23b9098d5bf0f91c31954001c2ff68">OVERLAY_BOX</a>)</td></tr>
<tr class="memdesc:af2f0dd5c2b47daa57953d3b5e1e83767"><td class="mdescLeft">&#160;</td><td class="mdescRight">Detect object locations in an RGBA image, into an array of the results allocated by the user.  <a href="#af2f0dd5c2b47daa57953d3b5e1e83767">More...</a><br /></td></tr>
<tr class="separator:af2f0dd5c2b47daa57953d3b5e1e83767"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a6bad305fe1207b5ad6a1f49e4879c158"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#a6bad305fe1207b5ad6a1f49e4879c158">Overlay</a> (float *input, float *output, uint32_t width, uint32_t height, <a class="el" href="structdetectNet_1_1Detection.html">Detection</a> *detections, uint32_t numDetections, uint32_t flags=<a class="el" href="classdetectNet.html#a29e74cde23a8dd541dbd848e457663d6aff23b9098d5bf0f91c31954001c2ff68">OVERLAY_BOX</a>)</td></tr>
<tr class="memdesc:a6bad305fe1207b5ad6a1f49e4879c158"><td class="mdescLeft">&#160;</td><td class="mdescRight">Draw the detected bounding boxes overlayed on an RGBA image.  <a href="#a6bad305fe1207b5ad6a1f49e4879c158">More...</a><br /></td></tr>
<tr class="separator:a6bad305fe1207b5ad6a1f49e4879c158"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad50b3774ee7350d0c256d94a785ba9df"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#ad50b3774ee7350d0c256d94a785ba9df">GetThreshold</a> () const</td></tr>
<tr class="memdesc:ad50b3774ee7350d0c256d94a785ba9df"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the minimum threshold for detection.  <a href="#ad50b3774ee7350d0c256d94a785ba9df">More...</a><br /></td></tr>
<tr class="separator:ad50b3774ee7350d0c256d94a785ba9df"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a1cb45695bf95d5fb5db48976d1de8ae2"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#a1cb45695bf95d5fb5db48976d1de8ae2">SetThreshold</a> (float threshold)</td></tr>
<tr class="memdesc:a1cb45695bf95d5fb5db48976d1de8ae2"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the minimum threshold for detection.  <a href="#a1cb45695bf95d5fb5db48976d1de8ae2">More...</a><br /></td></tr>
<tr class="separator:a1cb45695bf95d5fb5db48976d1de8ae2"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a38689b7d4b83723038677d66c1961974"><td class="memItemLeft" align="right" valign="top">uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#a38689b7d4b83723038677d66c1961974">GetMaxDetections</a> () const</td></tr>
<tr class="memdesc:a38689b7d4b83723038677d66c1961974"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the maximum number of simultaneous detections the network supports.  <a href="#a38689b7d4b83723038677d66c1961974">More...</a><br /></td></tr>
<tr class="separator:a38689b7d4b83723038677d66c1961974"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:acec72d01e020cf5bc50747abb0245f06"><td class="memItemLeft" align="right" valign="top">uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#acec72d01e020cf5bc50747abb0245f06">GetNumClasses</a> () const</td></tr>
<tr class="memdesc:acec72d01e020cf5bc50747abb0245f06"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the number of object classes supported in the detector.  <a href="#acec72d01e020cf5bc50747abb0245f06">More...</a><br /></td></tr>
<tr class="separator:acec72d01e020cf5bc50747abb0245f06"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a6aeee309257b1a42df275fdfe948a585"><td class="memItemLeft" align="right" valign="top">const char *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#a6aeee309257b1a42df275fdfe948a585">GetClassDesc</a> (uint32_t index) const</td></tr>
<tr class="memdesc:a6aeee309257b1a42df275fdfe948a585"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the description of a particular class.  <a href="#a6aeee309257b1a42df275fdfe948a585">More...</a><br /></td></tr>
<tr class="separator:a6aeee309257b1a42df275fdfe948a585"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a68322545826d7b866a70d5f9758f4d48"><td class="memItemLeft" align="right" valign="top">const char *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#a68322545826d7b866a70d5f9758f4d48">GetClassSynset</a> (uint32_t index) const</td></tr>
<tr class="memdesc:a68322545826d7b866a70d5f9758f4d48"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the class synset category of a particular class.  <a href="#a68322545826d7b866a70d5f9758f4d48">More...</a><br /></td></tr>
<tr class="separator:a68322545826d7b866a70d5f9758f4d48"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a8085235ab275b1bad3dcde14ec1223ed"><td class="memItemLeft" align="right" valign="top">const char *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#a8085235ab275b1bad3dcde14ec1223ed">GetClassPath</a> () const</td></tr>
<tr class="memdesc:a8085235ab275b1bad3dcde14ec1223ed"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the path to the file containing the class descriptions.  <a href="#a8085235ab275b1bad3dcde14ec1223ed">More...</a><br /></td></tr>
<tr class="separator:a8085235ab275b1bad3dcde14ec1223ed"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a93f21ec31720efe6b37de28c688449ac"><td class="memItemLeft" align="right" valign="top">float *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#a93f21ec31720efe6b37de28c688449ac">GetClassColor</a> (uint32_t classIndex) const</td></tr>
<tr class="memdesc:a93f21ec31720efe6b37de28c688449ac"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the RGBA visualization color a particular class.  <a href="#a93f21ec31720efe6b37de28c688449ac">More...</a><br /></td></tr>
<tr class="separator:a93f21ec31720efe6b37de28c688449ac"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ab9ba7c63d2fc417d0908324fd0dc2223"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#ab9ba7c63d2fc417d0908324fd0dc2223">SetClassColor</a> (uint32_t classIndex, float r, float g, float b, float a=255.0f)</td></tr>
<tr class="memdesc:ab9ba7c63d2fc417d0908324fd0dc2223"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the visualization color of a particular class of object.  <a href="#ab9ba7c63d2fc417d0908324fd0dc2223">More...</a><br /></td></tr>
<tr class="separator:ab9ba7c63d2fc417d0908324fd0dc2223"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac2ffd50b5fb52d6ceea09af29b144afc"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#ac2ffd50b5fb52d6ceea09af29b144afc">SetOverlayAlpha</a> (float alpha)</td></tr>
<tr class="memdesc:ac2ffd50b5fb52d6ceea09af29b144afc"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set overlay alpha blending value for all classes (between 0-255).  <a href="#ac2ffd50b5fb52d6ceea09af29b144afc">More...</a><br /></td></tr>
<tr class="separator:ac2ffd50b5fb52d6ceea09af29b144afc"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pub_methods_classtensorNet"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classtensorNet')"><img src="closed.png" alt="-"/>&#160;Public Member Functions inherited from <a class="el" href="classtensorNet.html">tensorNet</a></td></tr>
<tr class="memitem:ad19aafbfa262f9b8ffb0bff561f4d7f7 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#ad19aafbfa262f9b8ffb0bff561f4d7f7">~tensorNet</a> ()</td></tr>
<tr class="memdesc:ad19aafbfa262f9b8ffb0bff561f4d7f7 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Destory.  <a href="classtensorNet.html#ad19aafbfa262f9b8ffb0bff561f4d7f7">More...</a><br /></td></tr>
<tr class="separator:ad19aafbfa262f9b8ffb0bff561f4d7f7 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2e63d4670461814bd863ee0d9bd41526 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a2e63d4670461814bd863ee0d9bd41526">LoadNetwork</a> (const char *prototxt, const char *model, const char *mean=NULL, const char *input_blob=&quot;data&quot;, const char *output_blob=&quot;prob&quot;, uint32_t maxBatchSize=<a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowGPUFallback=true, nvinfer1::IInt8Calibrator *calibrator=NULL, cudaStream_t stream=NULL)</td></tr>
<tr class="memdesc:a2e63d4670461814bd863ee0d9bd41526 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a new network instance.  <a href="classtensorNet.html#a2e63d4670461814bd863ee0d9bd41526">More...</a><br /></td></tr>
<tr class="separator:a2e63d4670461814bd863ee0d9bd41526 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a0a06ffd12b465f39160f4a6925cccd9f inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a0a06ffd12b465f39160f4a6925cccd9f">LoadNetwork</a> (const char *prototxt, const char *model, const char *mean, const char *input_blob, const std::vector&lt; std::string &gt; &amp;output_blobs, uint32_t maxBatchSize=<a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowGPUFallback=true, nvinfer1::IInt8Calibrator *calibrator=NULL, cudaStream_t stream=NULL)</td></tr>
<tr class="memdesc:a0a06ffd12b465f39160f4a6925cccd9f inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a new network instance with multiple output layers.  <a href="classtensorNet.html#a0a06ffd12b465f39160f4a6925cccd9f">More...</a><br /></td></tr>
<tr class="separator:a0a06ffd12b465f39160f4a6925cccd9f inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a168c7f75c9fd6d264afd016e144f3878 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a168c7f75c9fd6d264afd016e144f3878">LoadNetwork</a> (const char *prototxt, const char *model, const char *mean, const char *input_blob, const <a class="el" href="tensorNet_8h.html#a64c8f3dfeacfa962ff9e23c586aedd1b">Dims3</a> &amp;input_dims, const std::vector&lt; std::string &gt; &amp;output_blobs, uint32_t maxBatchSize=<a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowGPUFallback=true, nvinfer1::IInt8Calibrator *calibrator=NULL, cudaStream_t stream=NULL)</td></tr>
<tr class="memdesc:a168c7f75c9fd6d264afd016e144f3878 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a new network instance (this variant is used for UFF models)  <a href="classtensorNet.html#a168c7f75c9fd6d264afd016e144f3878">More...</a><br /></td></tr>
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<tr class="memitem:a3413eb0ad4f240f457f192f39e2e03e8 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a3413eb0ad4f240f457f192f39e2e03e8">EnableLayerProfiler</a> ()</td></tr>
<tr class="memdesc:a3413eb0ad4f240f457f192f39e2e03e8 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Manually enable layer profiling times.  <a href="classtensorNet.html#a3413eb0ad4f240f457f192f39e2e03e8">More...</a><br /></td></tr>
<tr class="separator:a3413eb0ad4f240f457f192f39e2e03e8 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae49f74ff83e46112a30318fa0576cace inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#ae49f74ff83e46112a30318fa0576cace">EnableDebug</a> ()</td></tr>
<tr class="memdesc:ae49f74ff83e46112a30318fa0576cace inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Manually enable debug messages and synchronization.  <a href="classtensorNet.html#ae49f74ff83e46112a30318fa0576cace">More...</a><br /></td></tr>
<tr class="separator:ae49f74ff83e46112a30318fa0576cace inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7d0ec0d8504ac8b26c5ab4a6136599ca inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a7d0ec0d8504ac8b26c5ab4a6136599ca">AllowGPUFallback</a> () const</td></tr>
<tr class="memdesc:a7d0ec0d8504ac8b26c5ab4a6136599ca inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Return true if GPU fallback is enabled.  <a href="classtensorNet.html#a7d0ec0d8504ac8b26c5ab4a6136599ca">More...</a><br /></td></tr>
<tr class="separator:a7d0ec0d8504ac8b26c5ab4a6136599ca inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a92bb737172d26bda5f67d15346a02514 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top"><a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a92bb737172d26bda5f67d15346a02514">GetDevice</a> () const</td></tr>
<tr class="memdesc:a92bb737172d26bda5f67d15346a02514 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the device being used for execution.  <a href="classtensorNet.html#a92bb737172d26bda5f67d15346a02514">More...</a><br /></td></tr>
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<tr class="memitem:afb38b5f171025e987a00214cc4379ca9 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top"><a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#afb38b5f171025e987a00214cc4379ca9">GetPrecision</a> () const</td></tr>
<tr class="memdesc:afb38b5f171025e987a00214cc4379ca9 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the type of precision being used.  <a href="classtensorNet.html#afb38b5f171025e987a00214cc4379ca9">More...</a><br /></td></tr>
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<tr class="memitem:a6b8e8dba05bc5c677027913d8c64f259 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a6b8e8dba05bc5c677027913d8c64f259">IsPrecision</a> (<a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> type) const</td></tr>
<tr class="memdesc:a6b8e8dba05bc5c677027913d8c64f259 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Check if a particular precision is being used.  <a href="classtensorNet.html#a6b8e8dba05bc5c677027913d8c64f259">More...</a><br /></td></tr>
<tr class="separator:a6b8e8dba05bc5c677027913d8c64f259 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a34e350ec6185277ac09ae55a79403e62 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">cudaStream_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a34e350ec6185277ac09ae55a79403e62">GetStream</a> () const</td></tr>
<tr class="memdesc:a34e350ec6185277ac09ae55a79403e62 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the stream that the device is operating on.  <a href="classtensorNet.html#a34e350ec6185277ac09ae55a79403e62">More...</a><br /></td></tr>
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<tr class="memitem:a78cecfb7505be0ea59d29041abc85cbb inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">cudaStream_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a78cecfb7505be0ea59d29041abc85cbb">CreateStream</a> (bool nonBlocking=true)</td></tr>
<tr class="memdesc:a78cecfb7505be0ea59d29041abc85cbb inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Create and use a new stream for execution.  <a href="classtensorNet.html#a78cecfb7505be0ea59d29041abc85cbb">More...</a><br /></td></tr>
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<tr class="memitem:a679b177784c85bfdba63dcd1008ff633 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a679b177784c85bfdba63dcd1008ff633">SetStream</a> (cudaStream_t stream)</td></tr>
<tr class="memdesc:a679b177784c85bfdba63dcd1008ff633 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the stream that the device is operating on.  <a href="classtensorNet.html#a679b177784c85bfdba63dcd1008ff633">More...</a><br /></td></tr>
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<tr class="memitem:a624881afe27acd2b2fff0f0f75308ea2 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">const char *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a624881afe27acd2b2fff0f0f75308ea2">GetPrototxtPath</a> () const</td></tr>
<tr class="memdesc:a624881afe27acd2b2fff0f0f75308ea2 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the path to the network prototxt file.  <a href="classtensorNet.html#a624881afe27acd2b2fff0f0f75308ea2">More...</a><br /></td></tr>
<tr class="separator:a624881afe27acd2b2fff0f0f75308ea2 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac74d7f0571b7782b945ff85fd6894044 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">const char *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#ac74d7f0571b7782b945ff85fd6894044">GetModelPath</a> () const</td></tr>
<tr class="memdesc:ac74d7f0571b7782b945ff85fd6894044 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the path to the network model file.  <a href="classtensorNet.html#ac74d7f0571b7782b945ff85fd6894044">More...</a><br /></td></tr>
<tr class="separator:ac74d7f0571b7782b945ff85fd6894044 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:acfa7f1f01b46f658ffc96f8a002e8d48 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top"><a class="el" href="group__tensorNet.html#ga5d4597e0e7beae7133d542e220528725">modelType</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#acfa7f1f01b46f658ffc96f8a002e8d48">GetModelType</a> () const</td></tr>
<tr class="memdesc:acfa7f1f01b46f658ffc96f8a002e8d48 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the format of the network model.  <a href="classtensorNet.html#acfa7f1f01b46f658ffc96f8a002e8d48">More...</a><br /></td></tr>
<tr class="separator:acfa7f1f01b46f658ffc96f8a002e8d48 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a0a09d691ea080bd9734c5782c8fff6fd inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a0a09d691ea080bd9734c5782c8fff6fd">IsModelType</a> (<a class="el" href="group__tensorNet.html#ga5d4597e0e7beae7133d542e220528725">modelType</a> type) const</td></tr>
<tr class="memdesc:a0a09d691ea080bd9734c5782c8fff6fd inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Return true if the model is of the specified format.  <a href="classtensorNet.html#a0a09d691ea080bd9734c5782c8fff6fd">More...</a><br /></td></tr>
<tr class="separator:a0a09d691ea080bd9734c5782c8fff6fd inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a9dd2db089176ae6878e9ea7dd8fd80c3 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a9dd2db089176ae6878e9ea7dd8fd80c3">GetNetworkFPS</a> ()</td></tr>
<tr class="memdesc:a9dd2db089176ae6878e9ea7dd8fd80c3 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the network frames per second (FPS).  <a href="classtensorNet.html#a9dd2db089176ae6878e9ea7dd8fd80c3">More...</a><br /></td></tr>
<tr class="separator:a9dd2db089176ae6878e9ea7dd8fd80c3 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a49faef5920860345e503023b7c84423c inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a49faef5920860345e503023b7c84423c">GetNetworkTime</a> ()</td></tr>
<tr class="memdesc:a49faef5920860345e503023b7c84423c inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the network runtime (in milliseconds).  <a href="classtensorNet.html#a49faef5920860345e503023b7c84423c">More...</a><br /></td></tr>
<tr class="separator:a49faef5920860345e503023b7c84423c inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad266f93035a80dca80cd84d971e4f69b inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">float2&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#ad266f93035a80dca80cd84d971e4f69b">GetProfilerTime</a> (<a class="el" href="group__tensorNet.html#gae34d45c0faa674ef4cc0fbfc8fae5809">profilerQuery</a> query)</td></tr>
<tr class="memdesc:ad266f93035a80dca80cd84d971e4f69b inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the profiler runtime (in milliseconds).  <a href="classtensorNet.html#ad266f93035a80dca80cd84d971e4f69b">More...</a><br /></td></tr>
<tr class="separator:ad266f93035a80dca80cd84d971e4f69b inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a27cf81b3fecf93d2e63a61220a54b393 inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a27cf81b3fecf93d2e63a61220a54b393">GetProfilerTime</a> (<a class="el" href="group__tensorNet.html#gae34d45c0faa674ef4cc0fbfc8fae5809">profilerQuery</a> query, <a class="el" href="group__tensorNet.html#gaaa4127ed22c7165a32d0474ebf97975e">profilerDevice</a> device)</td></tr>
<tr class="memdesc:a27cf81b3fecf93d2e63a61220a54b393 inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Retrieve the profiler runtime (in milliseconds).  <a href="classtensorNet.html#a27cf81b3fecf93d2e63a61220a54b393">More...</a><br /></td></tr>
<tr class="separator:a27cf81b3fecf93d2e63a61220a54b393 inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:afc0f50abcf6ac71e96d51eba3ed53d4b inherit pub_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#afc0f50abcf6ac71e96d51eba3ed53d4b">PrintProfilerTimes</a> ()</td></tr>
<tr class="memdesc:afc0f50abcf6ac71e96d51eba3ed53d4b inherit pub_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Print the profiler times (in millseconds).  <a href="classtensorNet.html#afc0f50abcf6ac71e96d51eba3ed53d4b">More...</a><br /></td></tr>
<tr class="separator:afc0f50abcf6ac71e96d51eba3ed53d4b inherit pub_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-static-methods"></a>
Static Public Member Functions</h2></td></tr>
<tr class="memitem:ae3b1e96eef852854c0152d168f454f09"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classdetectNet.html#a8912b56ba825368c4911e315f4f207e4">NetworkType</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#ae3b1e96eef852854c0152d168f454f09">NetworkTypeFromStr</a> (const char *model_name)</td></tr>
<tr class="memdesc:ae3b1e96eef852854c0152d168f454f09"><td class="mdescLeft">&#160;</td><td class="mdescRight">Parse a string to one of the built-in pretrained models.  <a href="#ae3b1e96eef852854c0152d168f454f09">More...</a><br /></td></tr>
<tr class="separator:ae3b1e96eef852854c0152d168f454f09"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2bb2b446e26a466c9e355f1472f68e4c"><td class="memItemLeft" align="right" valign="top">static uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#a2bb2b446e26a466c9e355f1472f68e4c">OverlayFlagsFromStr</a> (const char *flags)</td></tr>
<tr class="memdesc:a2bb2b446e26a466c9e355f1472f68e4c"><td class="mdescLeft">&#160;</td><td class="mdescRight">Parse a string sequence into OverlayFlags enum.  <a href="#a2bb2b446e26a466c9e355f1472f68e4c">More...</a><br /></td></tr>
<tr class="separator:a2bb2b446e26a466c9e355f1472f68e4c"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac7c84410a8b08fc072bffc55f375e8a4"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classdetectNet.html">detectNet</a> *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#ac7c84410a8b08fc072bffc55f375e8a4">Create</a> (<a class="el" href="classdetectNet.html#a8912b56ba825368c4911e315f4f207e4">NetworkType</a> networkType=<a class="el" href="classdetectNet.html#a8912b56ba825368c4911e315f4f207e4abddff26f00980b719858a6cf6fb71f53">PEDNET_MULTI</a>, float threshold=<a class="el" href="group__detectNet.html#ga5de620e838c2ac9aec16c6de2977513f">DETECTNET_DEFAULT_THRESHOLD</a>, uint32_t maxBatchSize=<a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowGPUFallback=true)</td></tr>
<tr class="memdesc:ac7c84410a8b08fc072bffc55f375e8a4"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a new network instance.  <a href="#ac7c84410a8b08fc072bffc55f375e8a4">More...</a><br /></td></tr>
<tr class="separator:ac7c84410a8b08fc072bffc55f375e8a4"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a3979f7a074109d6e2b5c026d14758b81"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classdetectNet.html">detectNet</a> *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#a3979f7a074109d6e2b5c026d14758b81">Create</a> (const char *prototxt_path, const char *model_path, const char *mean_binary, const char *class_labels, float threshold=<a class="el" href="group__detectNet.html#ga5de620e838c2ac9aec16c6de2977513f">DETECTNET_DEFAULT_THRESHOLD</a>, const char *input=<a class="el" href="group__detectNet.html#gac824e329015dc8aed6e1112bfe21cb97">DETECTNET_DEFAULT_INPUT</a>, const char *coverage=<a class="el" href="group__detectNet.html#ga1e79603783719e4a79f2c68f1ef47621">DETECTNET_DEFAULT_COVERAGE</a>, const char *bboxes=<a class="el" href="group__detectNet.html#gac8c52a38ffa041865ed71fd2ea806620">DETECTNET_DEFAULT_BBOX</a>, uint32_t maxBatchSize=<a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowGPUFallback=true)</td></tr>
<tr class="memdesc:a3979f7a074109d6e2b5c026d14758b81"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a custom network instance.  <a href="#a3979f7a074109d6e2b5c026d14758b81">More...</a><br /></td></tr>
<tr class="separator:a3979f7a074109d6e2b5c026d14758b81"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:affcd98ca5c9bf9f53205df6a7838822c"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classdetectNet.html">detectNet</a> *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#affcd98ca5c9bf9f53205df6a7838822c">Create</a> (const char *prototxt_path, const char *model_path, float mean_pixel=0.0f, const char *class_labels=NULL, float threshold=DETECTNET_DEFAULT_THRESHOLD, const char *input=DETECTNET_DEFAULT_INPUT, const char *coverage=DETECTNET_DEFAULT_COVERAGE, const char *bboxes=DETECTNET_DEFAULT_BBOX, uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE, precisionType precision=TYPE_FASTEST, deviceType device=DEVICE_GPU, bool allowGPUFallback=true)</td></tr>
<tr class="memdesc:affcd98ca5c9bf9f53205df6a7838822c"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a custom network instance.  <a href="#affcd98ca5c9bf9f53205df6a7838822c">More...</a><br /></td></tr>
<tr class="separator:affcd98ca5c9bf9f53205df6a7838822c"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a761950971444cd0970143b3607c5cb06"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classdetectNet.html">detectNet</a> *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#a761950971444cd0970143b3607c5cb06">Create</a> (const char *model_path, const char *class_labels, float threshold, const char *input, const <a class="el" href="tensorNet_8h.html#a64c8f3dfeacfa962ff9e23c586aedd1b">Dims3</a> &amp;inputDims, const char *output, const char *numDetections, uint32_t maxBatchSize=<a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowGPUFallback=true)</td></tr>
<tr class="memdesc:a761950971444cd0970143b3607c5cb06"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a custom network instance of a UFF model.  <a href="#a761950971444cd0970143b3607c5cb06">More...</a><br /></td></tr>
<tr class="separator:a761950971444cd0970143b3607c5cb06"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aeca3a465ca3fa41d8e184025c0cbbd8c"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="classdetectNet.html">detectNet</a> *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#aeca3a465ca3fa41d8e184025c0cbbd8c">Create</a> (int argc, char **argv)</td></tr>
<tr class="memdesc:aeca3a465ca3fa41d8e184025c0cbbd8c"><td class="mdescLeft">&#160;</td><td class="mdescRight">Load a new network instance by parsing the command line.  <a href="#aeca3a465ca3fa41d8e184025c0cbbd8c">More...</a><br /></td></tr>
<tr class="separator:aeca3a465ca3fa41d8e184025c0cbbd8c"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a8c336e6b018a780e7ca74897d1bce628"><td class="memItemLeft" align="right" valign="top">static const char *&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#a8c336e6b018a780e7ca74897d1bce628">Usage</a> ()</td></tr>
<tr class="memdesc:a8c336e6b018a780e7ca74897d1bce628"><td class="mdescLeft">&#160;</td><td class="mdescRight">Usage string for command line arguments to <a class="el" href="classdetectNet.html#ac7c84410a8b08fc072bffc55f375e8a4" title="Load a new network instance. ">Create()</a>  <a href="#a8c336e6b018a780e7ca74897d1bce628">More...</a><br /></td></tr>
<tr class="separator:a8c336e6b018a780e7ca74897d1bce628"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pub_static_methods_classtensorNet"><td colspan="2" onclick="javascript:toggleInherit('pub_static_methods_classtensorNet')"><img src="closed.png" alt="-"/>&#160;Static Public Member Functions inherited from <a class="el" href="classtensorNet.html">tensorNet</a></td></tr>
<tr class="memitem:abe33fae5332296e2d917cb4ce435e255 inherit pub_static_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">static <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#abe33fae5332296e2d917cb4ce435e255">FindFastestPrecision</a> (<a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, bool allowInt8=true)</td></tr>
<tr class="memdesc:abe33fae5332296e2d917cb4ce435e255 inherit pub_static_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Determine the fastest native precision on a device.  <a href="classtensorNet.html#abe33fae5332296e2d917cb4ce435e255">More...</a><br /></td></tr>
<tr class="separator:abe33fae5332296e2d917cb4ce435e255 inherit pub_static_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae88436e652afdd7bceef7cb7c5fde7a6 inherit pub_static_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">static std::vector&lt; <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#ae88436e652afdd7bceef7cb7c5fde7a6">DetectNativePrecisions</a> (<a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>)</td></tr>
<tr class="memdesc:ae88436e652afdd7bceef7cb7c5fde7a6 inherit pub_static_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Detect the precisions supported natively on a device.  <a href="classtensorNet.html#ae88436e652afdd7bceef7cb7c5fde7a6">More...</a><br /></td></tr>
<tr class="separator:ae88436e652afdd7bceef7cb7c5fde7a6 inherit pub_static_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aa3bf1a3bf1fca38b39a200b4d8f727b2 inherit pub_static_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">static bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#aa3bf1a3bf1fca38b39a200b4d8f727b2">DetectNativePrecision</a> (const std::vector&lt; <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> &gt; &amp;nativeTypes, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> type)</td></tr>
<tr class="memdesc:aa3bf1a3bf1fca38b39a200b4d8f727b2 inherit pub_static_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Detect if a particular precision is supported natively.  <a href="classtensorNet.html#aa3bf1a3bf1fca38b39a200b4d8f727b2">More...</a><br /></td></tr>
<tr class="separator:aa3bf1a3bf1fca38b39a200b4d8f727b2 inherit pub_static_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7d72ec8bbaf61278ce533afd60d5391c inherit pub_static_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">static bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a7d72ec8bbaf61278ce533afd60d5391c">DetectNativePrecision</a> (<a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>)</td></tr>
<tr class="memdesc:a7d72ec8bbaf61278ce533afd60d5391c inherit pub_static_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Detect if a particular precision is supported natively.  <a href="classtensorNet.html#a7d72ec8bbaf61278ce533afd60d5391c">More...</a><br /></td></tr>
<tr class="separator:a7d72ec8bbaf61278ce533afd60d5391c inherit pub_static_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pro-methods"></a>
Protected Member Functions</h2></td></tr>
<tr class="memitem:a07a419d97f14aa0962b737e671ff438a"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#a07a419d97f14aa0962b737e671ff438a">detectNet</a> (float meanPixel=0.0f)</td></tr>
<tr class="separator:a07a419d97f14aa0962b737e671ff438a"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:acb367fb540a1dfb719c44ae0e4cdb045"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#acb367fb540a1dfb719c44ae0e4cdb045">allocDetections</a> ()</td></tr>
<tr class="separator:acb367fb540a1dfb719c44ae0e4cdb045"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a6213356e9907dc284e3212c42b346c62"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#a6213356e9907dc284e3212c42b346c62">defaultColors</a> ()</td></tr>
<tr class="separator:a6213356e9907dc284e3212c42b346c62"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a464e60c5fb80d1ab64928006c2a27702"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#a464e60c5fb80d1ab64928006c2a27702">defaultClassDesc</a> ()</td></tr>
<tr class="separator:a464e60c5fb80d1ab64928006c2a27702"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a812068e3db1f9f991e84a217e275b1e0"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#a812068e3db1f9f991e84a217e275b1e0">loadClassDesc</a> (const char *filename)</td></tr>
<tr class="separator:a812068e3db1f9f991e84a217e275b1e0"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a4d9b1b0fadbefe2b8e06e555906ae6ec"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#a4d9b1b0fadbefe2b8e06e555906ae6ec">init</a> (const char *prototxt_path, const char *model_path, const char *mean_binary, const char *class_labels, float threshold, const char *input, const char *coverage, const char *bboxes, uint32_t maxBatchSize, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device, bool allowGPUFallback)</td></tr>
<tr class="separator:a4d9b1b0fadbefe2b8e06e555906ae6ec"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ab95004897e18f73cff95a59796f17368"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#ab95004897e18f73cff95a59796f17368">clusterDetections</a> (<a class="el" href="structdetectNet_1_1Detection.html">Detection</a> *detections, uint32_t width, uint32_t height)</td></tr>
<tr class="separator:ab95004897e18f73cff95a59796f17368"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a4c1d9179adf3c75135605c622481e0dc"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#a4c1d9179adf3c75135605c622481e0dc">clusterDetections</a> (<a class="el" href="structdetectNet_1_1Detection.html">Detection</a> *detections, int n, float threshold=0.75f)</td></tr>
<tr class="separator:a4c1d9179adf3c75135605c622481e0dc"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae25ca7f51a3235011442ac1edeafe1d8"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#ae25ca7f51a3235011442ac1edeafe1d8">sortDetections</a> (<a class="el" href="structdetectNet_1_1Detection.html">Detection</a> *detections, int numDetections)</td></tr>
<tr class="separator:ae25ca7f51a3235011442ac1edeafe1d8"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pro_methods_classtensorNet"><td colspan="2" onclick="javascript:toggleInherit('pro_methods_classtensorNet')"><img src="closed.png" alt="-"/>&#160;Protected Member Functions inherited from <a class="el" href="classtensorNet.html">tensorNet</a></td></tr>
<tr class="memitem:ab6e617d96e5542bef023ee9d4c96388a inherit pro_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#ab6e617d96e5542bef023ee9d4c96388a">tensorNet</a> ()</td></tr>
<tr class="memdesc:ab6e617d96e5542bef023ee9d4c96388a inherit pro_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Constructor.  <a href="classtensorNet.html#ab6e617d96e5542bef023ee9d4c96388a">More...</a><br /></td></tr>
<tr class="separator:ab6e617d96e5542bef023ee9d4c96388a inherit pro_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a081b95210634e8ddb21e99d9ad1aa497 inherit pro_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a081b95210634e8ddb21e99d9ad1aa497">ProfileModel</a> (const std::string &amp;deployFile, const std::string &amp;modelFile, const char *input, const <a class="el" href="tensorNet_8h.html#a64c8f3dfeacfa962ff9e23c586aedd1b">Dims3</a> &amp;inputDims, const std::vector&lt; std::string &gt; &amp;outputs, uint32_t maxBatchSize, <a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision, <a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device, bool allowGPUFallback, nvinfer1::IInt8Calibrator *calibrator, std::ostream &amp;modelStream)</td></tr>
<tr class="memdesc:a081b95210634e8ddb21e99d9ad1aa497 inherit pro_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Create and output an optimized network model.  <a href="classtensorNet.html#a081b95210634e8ddb21e99d9ad1aa497">More...</a><br /></td></tr>
<tr class="separator:a081b95210634e8ddb21e99d9ad1aa497 inherit pro_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a088c3bf591e45e52ec227491f6f299ad inherit pro_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a088c3bf591e45e52ec227491f6f299ad">PROFILER_BEGIN</a> (<a class="el" href="group__tensorNet.html#gae34d45c0faa674ef4cc0fbfc8fae5809">profilerQuery</a> query)</td></tr>
<tr class="memdesc:a088c3bf591e45e52ec227491f6f299ad inherit pro_methods_classtensorNet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Begin a profiling query, before network is run.  <a href="classtensorNet.html#a088c3bf591e45e52ec227491f6f299ad">More...</a><br /></td></tr>
<tr class="separator:a088c3bf591e45e52ec227491f6f299ad inherit pro_methods_classtensorNet"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac8582b9a6099e3265da4c3f9fdf804ea inherit pro_methods_classtensorNet"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#ac8582b9a6099e3265da4c3f9fdf804ea">PROFILER_END</a> (<a class="el" href="group__tensorNet.html#gae34d45c0faa674ef4cc0fbfc8fae5809">profilerQuery</a> query)</td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pro-attribs"></a>
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<tr class="memitem:a8e7b5913f3f54d4bb0e6aa8e6071a74a inherit pro_attribs_classtensorNet"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a8e7b5913f3f54d4bb0e6aa8e6071a74a">mAllowGPUFallback</a></td></tr>
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<tr class="memitem:af7da0313dd945e81649e24b07e0fac0e inherit pro_attribs_classtensorNet"><td class="memItemLeft" align="right" valign="top"><a class="el" href="tensorNet_8h.html#a64c8f3dfeacfa962ff9e23c586aedd1b">Dims3</a>&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#af7da0313dd945e81649e24b07e0fac0e">mInputDims</a></td></tr>
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<tr class="memitem:a3487d6af48f91afcbeea76552d21d1c5 inherit pro_attribs_classtensorNet"><td class="memItemLeft" align="right" valign="top">std::vector&lt; <a class="el" href="structtensorNet_1_1outputLayer.html">outputLayer</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtensorNet.html#a3487d6af48f91afcbeea76552d21d1c5">mOutputs</a></td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pro-static-attribs"></a>
Static Protected Attributes</h2></td></tr>
<tr class="memitem:a77b8f8059780ab552d97a046c32e1f8a"><td class="memItemLeft" align="right" valign="top">static const uint32_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classdetectNet.html#a77b8f8059780ab552d97a046c32e1f8a">mNumDetectionSets</a> = 16</td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>Object recognition and localization networks with TensorRT support. </p>
</div><h2 class="groupheader">Member Enumeration Documentation</h2>
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<h2 class="memtitle"><span class="permalink"><a href="#a8912b56ba825368c4911e315f4f207e4">&#9670;&nbsp;</a></span>NetworkType</h2>

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<p>Network choice enumeration. </p>
<table class="fieldtable">
<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><a id="a8912b56ba825368c4911e315f4f207e4a46004cf6402155cfac9107356b5e7148"></a>CUSTOM&#160;</td><td class="fielddoc"><p>Custom model from user. </p>
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<tr><td class="fieldname"><a id="a8912b56ba825368c4911e315f4f207e4a0be700962b9af1014273d6c51274d717"></a>COCO_AIRPLANE&#160;</td><td class="fielddoc"><p>MS-COCO airplane class. </p>
</td></tr>
<tr><td class="fieldname"><a id="a8912b56ba825368c4911e315f4f207e4ab5ce7152df406d0e699fb47c29cda175"></a>COCO_BOTTLE&#160;</td><td class="fielddoc"><p>MS-COCO bottle class. </p>
</td></tr>
<tr><td class="fieldname"><a id="a8912b56ba825368c4911e315f4f207e4aa9c3e7f707f38390b8ebe551a6844efd"></a>COCO_CHAIR&#160;</td><td class="fielddoc"><p>MS-COCO chair class. </p>
</td></tr>
<tr><td class="fieldname"><a id="a8912b56ba825368c4911e315f4f207e4afb5c869902acb688cf8b4d7e334bc0f4"></a>COCO_DOG&#160;</td><td class="fielddoc"><p>MS-COCO dog class. </p>
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<tr><td class="fieldname"><a id="a8912b56ba825368c4911e315f4f207e4aba129790ea4695b8fc94f0eeecb344de"></a>FACENET&#160;</td><td class="fielddoc"><p>Human facial detector trained on FDDB. </p>
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<tr><td class="fieldname"><a id="a8912b56ba825368c4911e315f4f207e4a6e6da8c4ab8ff441042f7922082be8c3"></a>PEDNET&#160;</td><td class="fielddoc"><p>Pedestrian / person detector. </p>
</td></tr>
<tr><td class="fieldname"><a id="a8912b56ba825368c4911e315f4f207e4abddff26f00980b719858a6cf6fb71f53"></a>PEDNET_MULTI&#160;</td><td class="fielddoc"><p>Multi-class pedestrian + baggage detector. </p>
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<h2 class="memtitle"><span class="permalink"><a href="#a29e74cde23a8dd541dbd848e457663d6">&#9670;&nbsp;</a></span>OverlayFlags</h2>

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<p>Overlay flags (can be OR'd together). </p>
<table class="fieldtable">
<tr><th colspan="2">Enumerator</th></tr><tr><td class="fieldname"><a id="a29e74cde23a8dd541dbd848e457663d6a966eaaaa69ed08d36dcb862e3fc644bf"></a>OVERLAY_NONE&#160;</td><td class="fielddoc"><p>No overlay. </p>
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<tr><td class="fieldname"><a id="a29e74cde23a8dd541dbd848e457663d6aff23b9098d5bf0f91c31954001c2ff68"></a>OVERLAY_BOX&#160;</td><td class="fielddoc"><p>Overlay the object bounding boxes. </p>
</td></tr>
<tr><td class="fieldname"><a id="a29e74cde23a8dd541dbd848e457663d6a7eb5454e7a6dd53d90c2ddd33b00d049"></a>OVERLAY_LABEL&#160;</td><td class="fielddoc"><p>Overlay the class description labels. </p>
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<tr><td class="fieldname"><a id="a29e74cde23a8dd541dbd848e457663d6af7ce69cd21d84fb3b963713da66756eb"></a>OVERLAY_CONFIDENCE&#160;</td><td class="fielddoc"><p>Overlay the detection confidence values. </p>
</td></tr>
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<h2 class="groupheader">Constructor &amp; Destructor Documentation</h2>
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<h2 class="memtitle"><span class="permalink"><a href="#a82c0e177e0da142fa22aa7580077307d">&#9670;&nbsp;</a></span>~detectNet()</h2>

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<p>Destory. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a07a419d97f14aa0962b737e671ff438a">&#9670;&nbsp;</a></span>detectNet()</h2>

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          <td class="paramtype">float&#160;</td>
          <td class="paramname"><em>meanPixel</em> = <code>0.0f</code></td><td>)</td>
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<h2 class="groupheader">Member Function Documentation</h2>
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<h2 class="memtitle"><span class="permalink"><a href="#acb367fb540a1dfb719c44ae0e4cdb045">&#9670;&nbsp;</a></span>allocDetections()</h2>

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<h2 class="memtitle"><span class="permalink"><a href="#ab95004897e18f73cff95a59796f17368">&#9670;&nbsp;</a></span>clusterDetections() <span class="overload">[1/2]</span></h2>

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          <td class="paramtype"><a class="el" href="structdetectNet_1_1Detection.html">Detection</a> *&#160;</td>
          <td class="paramname"><em>detections</em>, </td>
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          <td class="paramkey"></td>
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          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>width</em>, </td>
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          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>height</em>&#160;</td>
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<h2 class="memtitle"><span class="permalink"><a href="#a4c1d9179adf3c75135605c622481e0dc">&#9670;&nbsp;</a></span>clusterDetections() <span class="overload">[2/2]</span></h2>

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          <td class="paramtype"><a class="el" href="structdetectNet_1_1Detection.html">Detection</a> *&#160;</td>
          <td class="paramname"><em>detections</em>, </td>
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          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>n</em>, </td>
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          <td class="paramtype">float&#160;</td>
          <td class="paramname"><em>threshold</em> = <code>0.75f</code>&#160;</td>
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<h2 class="memtitle"><span class="permalink"><a href="#ac7c84410a8b08fc072bffc55f375e8a4">&#9670;&nbsp;</a></span>Create() <span class="overload">[1/5]</span></h2>

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          <td>(</td>
          <td class="paramtype"><a class="el" href="classdetectNet.html#a8912b56ba825368c4911e315f4f207e4">NetworkType</a>&#160;</td>
          <td class="paramname"><em>networkType</em> = <code><a class="el" href="classdetectNet.html#a8912b56ba825368c4911e315f4f207e4abddff26f00980b719858a6cf6fb71f53">PEDNET_MULTI</a></code>, </td>
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          <td class="paramtype">float&#160;</td>
          <td class="paramname"><em>threshold</em> = <code><a class="el" href="group__detectNet.html#ga5de620e838c2ac9aec16c6de2977513f">DETECTNET_DEFAULT_THRESHOLD</a></code>, </td>
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          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>maxBatchSize</em> = <code><a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a></code>, </td>
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          <td class="paramtype"><a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a>&#160;</td>
          <td class="paramname"><em>precision</em> = <code><a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a></code>, </td>
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          <td class="paramtype"><a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a>&#160;</td>
          <td class="paramname"><em>device</em> = <code><a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a></code>, </td>
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<p>Load a new network instance. </p>
<dl class="params"><dt>Parameters</dt><dd>
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    <tr><td class="paramname">networkType</td><td>type of pre-supported network to load </td></tr>
    <tr><td class="paramname">threshold</td><td>default minimum threshold for detection </td></tr>
    <tr><td class="paramname">maxBatchSize</td><td>The maximum batch size that the network will support and be optimized for. </td></tr>
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<h2 class="memtitle"><span class="permalink"><a href="#a3979f7a074109d6e2b5c026d14758b81">&#9670;&nbsp;</a></span>Create() <span class="overload">[2/5]</span></h2>

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          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>prototxt_path</em>, </td>
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          <td class="paramtype">const char *&#160;</td>
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          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>class_labels</em>, </td>
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          <td class="paramtype">float&#160;</td>
          <td class="paramname"><em>threshold</em> = <code><a class="el" href="group__detectNet.html#ga5de620e838c2ac9aec16c6de2977513f">DETECTNET_DEFAULT_THRESHOLD</a></code>, </td>
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          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>input</em> = <code><a class="el" href="group__detectNet.html#gac824e329015dc8aed6e1112bfe21cb97">DETECTNET_DEFAULT_INPUT</a></code>, </td>
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          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>coverage</em> = <code><a class="el" href="group__detectNet.html#ga1e79603783719e4a79f2c68f1ef47621">DETECTNET_DEFAULT_COVERAGE</a></code>, </td>
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          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>bboxes</em> = <code><a class="el" href="group__detectNet.html#gac8c52a38ffa041865ed71fd2ea806620">DETECTNET_DEFAULT_BBOX</a></code>, </td>
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          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>maxBatchSize</em> = <code><a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a></code>, </td>
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          <td></td>
          <td class="paramtype"><a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a>&#160;</td>
          <td class="paramname"><em>precision</em> = <code><a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a></code>, </td>
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          <td class="paramtype"><a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a>&#160;</td>
          <td class="paramname"><em>device</em> = <code><a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a></code>, </td>
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          <td class="paramtype">bool&#160;</td>
          <td class="paramname"><em>allowGPUFallback</em> = <code>true</code>&#160;</td>
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<p>Load a custom network instance. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">prototxt_path</td><td>File path to the deployable network prototxt </td></tr>
    <tr><td class="paramname">model_path</td><td>File path to the caffemodel </td></tr>
    <tr><td class="paramname">mean_binary</td><td>File path to the mean value binary proto </td></tr>
    <tr><td class="paramname">class_labels</td><td>File path to list of class name labels </td></tr>
    <tr><td class="paramname">threshold</td><td>default minimum threshold for detection </td></tr>
    <tr><td class="paramname">input</td><td>Name of the input layer blob. </td></tr>
    <tr><td class="paramname">coverage</td><td>Name of the output coverage classifier layer blob, which contains the confidence values for each bbox. </td></tr>
    <tr><td class="paramname">bboxes</td><td>Name of the output bounding box layer blob, which contains a grid of rectangles in the image. </td></tr>
    <tr><td class="paramname">maxBatchSize</td><td>The maximum batch size that the network will support and be optimized for. </td></tr>
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          <td class="memname">static <a class="el" href="classdetectNet.html">detectNet</a>* detectNet::Create </td>
          <td>(</td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>prototxt_path</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>model_path</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">float&#160;</td>
          <td class="paramname"><em>mean_pixel</em> = <code>0.0f</code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>class_labels</em> = <code>NULL</code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">float&#160;</td>
          <td class="paramname"><em>threshold</em> = <code><a class="el" href="group__detectNet.html#ga5de620e838c2ac9aec16c6de2977513f">DETECTNET_DEFAULT_THRESHOLD</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>input</em> = <code><a class="el" href="group__detectNet.html#gac824e329015dc8aed6e1112bfe21cb97">DETECTNET_DEFAULT_INPUT</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>coverage</em> = <code><a class="el" href="group__detectNet.html#ga1e79603783719e4a79f2c68f1ef47621">DETECTNET_DEFAULT_COVERAGE</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>bboxes</em> = <code><a class="el" href="group__detectNet.html#gac8c52a38ffa041865ed71fd2ea806620">DETECTNET_DEFAULT_BBOX</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>maxBatchSize</em> = <code><a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a>&#160;</td>
          <td class="paramname"><em>precision</em> = <code><a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a>&#160;</td>
          <td class="paramname"><em>device</em> = <code><a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">bool&#160;</td>
          <td class="paramname"><em>allowGPUFallback</em> = <code>true</code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
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<p>Load a custom network instance. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">prototxt_path</td><td>File path to the deployable network prototxt </td></tr>
    <tr><td class="paramname">model_path</td><td>File path to the caffemodel </td></tr>
    <tr><td class="paramname">mean_pixel</td><td>Input transform subtraction value (use 0.0 if the network already does this) </td></tr>
    <tr><td class="paramname">class_labels</td><td>File path to list of class name labels </td></tr>
    <tr><td class="paramname">threshold</td><td>default minimum threshold for detection </td></tr>
    <tr><td class="paramname">input</td><td>Name of the input layer blob. </td></tr>
    <tr><td class="paramname">coverage</td><td>Name of the output coverage classifier layer blob, which contains the confidence values for each bbox. </td></tr>
    <tr><td class="paramname">bboxes</td><td>Name of the output bounding box layer blob, which contains a grid of rectangles in the image. </td></tr>
    <tr><td class="paramname">maxBatchSize</td><td>The maximum batch size that the network will support and be optimized for. </td></tr>
  </table>
  </dd>
</dl>

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<a id="a761950971444cd0970143b3607c5cb06"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a761950971444cd0970143b3607c5cb06">&#9670;&nbsp;</a></span>Create() <span class="overload">[4/5]</span></h2>

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          <td class="memname">static <a class="el" href="classdetectNet.html">detectNet</a>* detectNet::Create </td>
          <td>(</td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>model_path</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>class_labels</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">float&#160;</td>
          <td class="paramname"><em>threshold</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const <a class="el" href="tensorNet_8h.html#a64c8f3dfeacfa962ff9e23c586aedd1b">Dims3</a> &amp;&#160;</td>
          <td class="paramname"><em>inputDims</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>output</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const char *&#160;</td>
          <td class="paramname"><em>numDetections</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>maxBatchSize</em> = <code><a class="el" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a>&#160;</td>
          <td class="paramname"><em>precision</em> = <code><a class="el" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a>&#160;</td>
          <td class="paramname"><em>device</em> = <code><a class="el" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">bool&#160;</td>
          <td class="paramname"><em>allowGPUFallback</em> = <code>true</code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
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<p>Load a custom network instance of a UFF model. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">model_path</td><td>File path to the UFF model </td></tr>
    <tr><td class="paramname">class_labels</td><td>File path to list of class name labels </td></tr>
    <tr><td class="paramname">threshold</td><td>default minimum threshold for detection </td></tr>
    <tr><td class="paramname">input</td><td>Name of the input layer blob. </td></tr>
    <tr><td class="paramname">inputDims</td><td>Dimensions of the input layer blob. </td></tr>
    <tr><td class="paramname">output</td><td>Name of the output layer blob containing the bounding boxes, ect. </td></tr>
    <tr><td class="paramname">numDetections</td><td>Name of the output layer blob containing the detection count. </td></tr>
    <tr><td class="paramname">maxBatchSize</td><td>The maximum batch size that the network will support and be optimized for. </td></tr>
  </table>
  </dd>
</dl>

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<h2 class="memtitle"><span class="permalink"><a href="#aeca3a465ca3fa41d8e184025c0cbbd8c">&#9670;&nbsp;</a></span>Create() <span class="overload">[5/5]</span></h2>

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          <td class="memname">static <a class="el" href="classdetectNet.html">detectNet</a>* detectNet::Create </td>
          <td>(</td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>argc</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">char **&#160;</td>
          <td class="paramname"><em>argv</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
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<p>Load a new network instance by parsing the command line. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a464e60c5fb80d1ab64928006c2a27702">&#9670;&nbsp;</a></span>defaultClassDesc()</h2>

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          <td class="memname">void detectNet::defaultClassDesc </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td></td>
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<span class="mlabels"><span class="mlabel">protected</span></span>  </td>
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<h2 class="memtitle"><span class="permalink"><a href="#a6213356e9907dc284e3212c42b346c62">&#9670;&nbsp;</a></span>defaultColors()</h2>

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          <td class="memname">bool detectNet::defaultColors </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td></td>
        </tr>
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  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">protected</span></span>  </td>
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</div><div class="memdoc">

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<a id="ac2144a8af64dce65e391ccb6ab0fb5f9"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ac2144a8af64dce65e391ccb6ab0fb5f9">&#9670;&nbsp;</a></span>Detect() <span class="overload">[1/2]</span></h2>

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          <td class="memname">int detectNet::Detect </td>
          <td>(</td>
          <td class="paramtype">float *&#160;</td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>width</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>height</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="structdetectNet_1_1Detection.html">Detection</a> **&#160;</td>
          <td class="paramname"><em>detections</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>overlay</em> = <code><a class="el" href="classdetectNet.html#a29e74cde23a8dd541dbd848e457663d6aff23b9098d5bf0f91c31954001c2ff68">OVERLAY_BOX</a></code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
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<p>Detect object locations from an RGBA image, returning an array containing the detection results. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>float4 RGBA input image in CUDA device memory. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">width</td><td>width of the input image in pixels. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">height</td><td>height of the input image in pixels. </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">detections</td><td>pointer that will be set to array of detection results (residing in shared CPU/GPU memory) </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">overlay</td><td>bitwise OR combination of overlay flags (</td></tr>
  </table>
  </dd>
</dl>
<dl class="section see"><dt>See also</dt><dd><a class="el" href="classdetectNet.html#a29e74cde23a8dd541dbd848e457663d6" title="Overlay flags (can be OR&#39;d together). ">OverlayFlags</a> and </dd>
<dd>
<a class="el" href="classdetectNet.html#a6bad305fe1207b5ad6a1f49e4879c158" title="Draw the detected bounding boxes overlayed on an RGBA image. ">Overlay()</a>), or <a class="el" href="classdetectNet.html#a29e74cde23a8dd541dbd848e457663d6a966eaaaa69ed08d36dcb862e3fc644bf" title="No overlay. ">OVERLAY_NONE</a>. </dd></dl>
<dl class="section return"><dt>Returns</dt><dd>The number of detected objects, 0 if there were no detected objects, and -1 if an error was encountered. </dd></dl>

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<h2 class="memtitle"><span class="permalink"><a href="#af2f0dd5c2b47daa57953d3b5e1e83767">&#9670;&nbsp;</a></span>Detect() <span class="overload">[2/2]</span></h2>

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          <td class="memname">int detectNet::Detect </td>
          <td>(</td>
          <td class="paramtype">float *&#160;</td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>width</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>height</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="structdetectNet_1_1Detection.html">Detection</a> *&#160;</td>
          <td class="paramname"><em>detections</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>overlay</em> = <code><a class="el" href="classdetectNet.html#a29e74cde23a8dd541dbd848e457663d6aff23b9098d5bf0f91c31954001c2ff68">OVERLAY_BOX</a></code>&#160;</td>
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          <td></td>
          <td>)</td>
          <td></td><td></td>
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<p>Detect object locations in an RGBA image, into an array of the results allocated by the user. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">input</td><td>float4 RGBA input image in CUDA device memory. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">width</td><td>width of the input image in pixels. </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">height</td><td>height of the input image in pixels. </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">detections</td><td>pointer to user-allocated array that will be filled with the detection results. </td></tr>
  </table>
  </dd>
</dl>
<dl class="section see"><dt>See also</dt><dd><a class="el" href="classdetectNet.html#a38689b7d4b83723038677d66c1961974" title="Retrieve the maximum number of simultaneous detections the network supports. ">GetMaxDetections()</a> for the number of detection results that should be allocated in this buffer. </dd></dl>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">overlay</td><td>bitwise OR combination of overlay flags (</td></tr>
  </table>
  </dd>
</dl>
<dl class="section see"><dt>See also</dt><dd><a class="el" href="classdetectNet.html#a29e74cde23a8dd541dbd848e457663d6" title="Overlay flags (can be OR&#39;d together). ">OverlayFlags</a> and </dd>
<dd>
<a class="el" href="classdetectNet.html#a6bad305fe1207b5ad6a1f49e4879c158" title="Draw the detected bounding boxes overlayed on an RGBA image. ">Overlay()</a>), or <a class="el" href="classdetectNet.html#a29e74cde23a8dd541dbd848e457663d6a966eaaaa69ed08d36dcb862e3fc644bf" title="No overlay. ">OVERLAY_NONE</a>. </dd></dl>
<dl class="section return"><dt>Returns</dt><dd>The number of detected objects, 0 if there were no detected objects, and -1 if an error was encountered. </dd></dl>

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<h2 class="memtitle"><span class="permalink"><a href="#a93f21ec31720efe6b37de28c688449ac">&#9670;&nbsp;</a></span>GetClassColor()</h2>

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          <td class="memname">float* detectNet::GetClassColor </td>
          <td>(</td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>classIndex</em></td><td>)</td>
          <td> const</td>
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<p>Retrieve the RGBA visualization color a particular class. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a6aeee309257b1a42df275fdfe948a585">&#9670;&nbsp;</a></span>GetClassDesc()</h2>

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          <td class="memname">const char* detectNet::GetClassDesc </td>
          <td>(</td>
          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>index</em></td><td>)</td>
          <td> const</td>
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<p>Retrieve the description of a particular class. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a8085235ab275b1bad3dcde14ec1223ed">&#9670;&nbsp;</a></span>GetClassPath()</h2>

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          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td> const</td>
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<p>Retrieve the path to the file containing the class descriptions. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a68322545826d7b866a70d5f9758f4d48">&#9670;&nbsp;</a></span>GetClassSynset()</h2>

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          <td class="paramname"><em>index</em></td><td>)</td>
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<p>Retrieve the class synset category of a particular class. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a38689b7d4b83723038677d66c1961974">&#9670;&nbsp;</a></span>GetMaxDetections()</h2>

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<p>Retrieve the maximum number of simultaneous detections the network supports. </p>
<p>Knowing this is useful for allocating the buffers to store the output detection results. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#acec72d01e020cf5bc50747abb0245f06">&#9670;&nbsp;</a></span>GetNumClasses()</h2>

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<p>Retrieve the number of object classes supported in the detector. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#ad50b3774ee7350d0c256d94a785ba9df">&#9670;&nbsp;</a></span>GetThreshold()</h2>

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<p>Retrieve the minimum threshold for detection. </p>
<p>TODO: change this to per-class in the future </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a4d9b1b0fadbefe2b8e06e555906ae6ec">&#9670;&nbsp;</a></span>init()</h2>

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          <td class="paramname"><em>bboxes</em>, </td>
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          <td class="paramtype">uint32_t&#160;</td>
          <td class="paramname"><em>maxBatchSize</em>, </td>
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          <td class="paramtype"><a class="el" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a>&#160;</td>
          <td class="paramname"><em>precision</em>, </td>
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          <td class="paramtype"><a class="el" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a>&#160;</td>
          <td class="paramname"><em>device</em>, </td>
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          <td class="paramtype">bool&#160;</td>
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<h2 class="memtitle"><span class="permalink"><a href="#a812068e3db1f9f991e84a217e275b1e0">&#9670;&nbsp;</a></span>loadClassDesc()</h2>

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<h2 class="memtitle"><span class="permalink"><a href="#ae3b1e96eef852854c0152d168f454f09">&#9670;&nbsp;</a></span>NetworkTypeFromStr()</h2>

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<p>Parse a string to one of the built-in pretrained models. </p>
<p>Valid names are "pednet", "multiped", "facenet", "face", "coco-airplane", "airplane", "coco-bottle", "bottle", "coco-chair", "chair", "coco-dog", or "dog". </p><dl class="section return"><dt>Returns</dt><dd>one of the <a class="el" href="classdetectNet.html#a8912b56ba825368c4911e315f4f207e4" title="Network choice enumeration. ">detectNet::NetworkType</a> enums, or <a class="el" href="classdetectNet.html#a8912b56ba825368c4911e315f4f207e4a46004cf6402155cfac9107356b5e7148" title="Custom model from user. ">detectNet::CUSTOM</a> on invalid string. </dd></dl>

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<h2 class="memtitle"><span class="permalink"><a href="#a6bad305fe1207b5ad6a1f49e4879c158">&#9670;&nbsp;</a></span>Overlay()</h2>

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<p>Draw the detected bounding boxes overlayed on an RGBA image. </p>
<dl class="section note"><dt>Note</dt><dd><a class="el" href="classdetectNet.html#a6bad305fe1207b5ad6a1f49e4879c158" title="Draw the detected bounding boxes overlayed on an RGBA image. ">Overlay()</a> will automatically be called by default by <a class="el" href="classdetectNet.html#ac2144a8af64dce65e391ccb6ab0fb5f9" title="Detect object locations from an RGBA image, returning an array containing the detection results...">Detect()</a>, if the overlay parameter is true </dd></dl>
<dl class="params"><dt>Parameters</dt><dd>
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    <tr><td class="paramname">output</td><td>float4 RGBA output image in CUDA device memory. </td></tr>
    <tr><td class="paramname">detections</td><td>Array of detections allocated in CUDA device memory. </td></tr>
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<h2 class="memtitle"><span class="permalink"><a href="#a2bb2b446e26a466c9e355f1472f68e4c">&#9670;&nbsp;</a></span>OverlayFlagsFromStr()</h2>

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<p>Parse a string sequence into OverlayFlags enum. </p>
<p>Valid flags are "none", "box", "label", and "conf" and it is possible to combine flags (bitwise OR) together with commas or pipe (|) symbol. For example, the string sequence "box,label,conf" would return the flags <code>OVERLAY_BOX|OVERLAY_LABEL|OVERLAY_CONFIDENCE</code>. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#ab9ba7c63d2fc417d0908324fd0dc2223">&#9670;&nbsp;</a></span>SetClassColor()</h2>

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<p>Set the visualization color of a particular class of object. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#ac2ffd50b5fb52d6ceea09af29b144afc">&#9670;&nbsp;</a></span>SetOverlayAlpha()</h2>

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<p>Set overlay alpha blending value for all classes (between 0-255). </p>

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<h2 class="memtitle"><span class="permalink"><a href="#a1cb45695bf95d5fb5db48976d1de8ae2">&#9670;&nbsp;</a></span>SetThreshold()</h2>

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<p>Set the minimum threshold for detection. </p>

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<h2 class="memtitle"><span class="permalink"><a href="#ae25ca7f51a3235011442ac1edeafe1d8">&#9670;&nbsp;</a></span>sortDetections()</h2>

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<h2 class="memtitle"><span class="permalink"><a href="#a8c336e6b018a780e7ca74897d1bce628">&#9670;&nbsp;</a></span>Usage()</h2>

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<p>Usage string for command line arguments to <a class="el" href="classdetectNet.html#ac7c84410a8b08fc072bffc55f375e8a4" title="Load a new network instance. ">Create()</a> </p>

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<h2 class="groupheader">Member Data Documentation</h2>
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<h2 class="memtitle"><span class="permalink"><a href="#adbbb6ba9ae382abe03347333cff66fca">&#9670;&nbsp;</a></span>mClassColors</h2>

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<h2 class="memtitle"><span class="permalink"><a href="#a1c85b56e7b1e4b3e474f47c7f18a157d">&#9670;&nbsp;</a></span>mClassSynset</h2>

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<h2 class="memtitle"><span class="permalink"><a href="#a2a95020a83b2c33353450505355af0ca">&#9670;&nbsp;</a></span>mCoverageThreshold</h2>

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<hr/>The documentation for this class was generated from the following file:<ul>
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